Kubernetes Researcher

ABOUT POINTFIVE PointFive finds the cost buried inside enterprise infrastructure, from the cloud to coding agents. We pioneered DeepWaste across cloud and data platforms, and we are now doing the same for AI token spend with TokenShift. Fanatics, H&M, Hertz, Nubank, Citizens Bank and other Fortune 500 companies use PointFive to see and cut what their infrastructure and AI actually cost. We have raised $96 million, most recently a $60 million Series B led by Accel https://www.pointfive.co/press/pointfive-raises-60m-series-b. Engineering sits in Tel Aviv and our go-to-market team is focused on North America. PointFive Labs, our research arm, publishes original work on how infrastructure and AI actually get paid for. CRN named us one of the 10 hottest cloud computing startups of 2026 https://www.crn.com/news/cloud/2026/the-10-hottest-cloud-computing-startups-of-2026-so-far?page=9, Forbes Israel put us on its Next Billion Dollar Startups list https://forbes.co.il/rankings/2025nextbilliondollarstartups/, and Redpoint selected us for its InfraRed 100 https://www.pointfive.co/press/pointfive-infrared-100, its ranking of the private companies shaping the future of cloud infrastructure. THE ROLE We're looking for a researcher who has run real production workloads on Kubernetes and wants to turn that experience into detections that thousands of engineers will trust. You'll own Kubernetes cost efficiency end to end: it starts with "this looks expensive," moves through real billing, metrics and inventory data, and finishes with a shipped detection that gets adopted by verity of engineering teams who act on it. This role sits at the junction of research, product and engineering: you pick the problem, prove it against real customer data, and take it to production. You’ll set the standard and best practices for k8s efficiency. The detections you develop will run in a variety of complex Kubernetes environments across the planet, and engineering teams everywhere will build on what you discover. WHAT YOU'LL DO - Research and ship new Kubernetes detections. Find optimization opportunities, prove them against real customer data - Lead the Kubernetes domain end to end: the detection library and build the road map to maximize value. - Work hand-in-hand with engineering, and go deep wherever the problem takes you: Kubernetes internals, cloud provider APIs, billing systems, data pipelines. This is a technical role, not a research-only one. - Bring domain expertise into pre-sales and post-sales processes, directly and tightly working with prospects to maximize value - Publish thought-leadership content on Kubernetes cost efficiency: blog posts, white papers, OSS, webinars or live sessions. MUST HAVE - 3+ years running production workloads on Kubernetes (EKS, GKE or AKS) as an SRE, platform, DevOps or infrastructure engineer. You've been on-call for it, and you know how requests and limits drive scheduling and pod packing, how the cluster autoscaler and Karpenter behave under real load, why a node group's minimum size is set where it is, and what breaks when you change instance architecture or generation. - Independent and comfortable owning a ambiguity problem e2e from first hypothesis to shipped detection. - Ability to orchestrate engineering work: translate research findings into clear technical requirements, align priorities with engineering teams, and coordinate delivery through to production. - Deep, hands-on AWS knowledge (EC2, EKS, ECR, networking, Savings Plans, RIs, and more), and working knowledge of Azure or GCP. - Strong quantitative skills: analytics, statistics, modeling. You can take complex, unstructured data, build the right model or calculation for it, and know exactly why the numbers hold up. - A skeptical, evidence-first mindset: you look for the reason a recommendation is wrong before you look for the reason it's right. - Clear communication skills. You can explain a technical finding and its business impact to different stakeholders. NICE TO HAVE - Experience running AI/ML infrastructure: GPU node pools, inference serving, or managed model platforms (Bedrock, Azure OpenAI, Vertex AI), including per-token and provisioned-throughput pricing. - Hands-on FinOps experience: AWS CUR, Split Cost Allocation Data, FOCUS, cost allocation and chargeback. - Hands-on experience in dbt, Snowflake, GO - Experience with ARM/Graviton migrations or large-scale rightsizing programs. - Data platform experience (Databricks, Snowflake, BigQuery). - Published technical research artifacts (blog/white papers/OSS) or experience in public speaking (conferences, meetups). - Contributions to related open-source projects. - Prior cloud security research experience. - Academic background SUCCESS IN YOUR FIRST 6 MONTHS - You own Kubernetes cost efficiency as an area and have shipped several new detections in it. - Customer teams have reviewed, approved and implemented your recommendations across their environments, and the findings held. WHY JOIN - Your work reaches engineering teams that spend millions a month on cloud, and they act on it. - You'll work with real cost, inventory and metrics data from hundreds of production environments on every major cloud, so you'll see patterns no single company sees. - A small research team with high ownership: you pick the problem, prove it, and solve it. - Go deep You will study Kubernetes environments in full depth. You will define the best practices other teams follow. - A unique opportunity. No other company gives you this exact mix: real Kubernetes research, production-scale data, and direct customer impact.